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Training specific deep learning models for particular tasks is common acrossvarious domains within seismology. However, this approach encounters twolimitations: inadequate labeled data for certain tasks and limitedgeneralization across regions. To address these challenges, we developSeisCLIP, a seismology foundation model trained through contrastive learningfrom multi-modal data. It consists of a transformer encoder for extractingcrucial features from time-frequency seismic spectrum and an MLP encoder forintegrating the phase and source information of the same event. These encodersare jointly pre-trained on a vast dataset and the spectrum encoder issubsequently fine-tuned on smaller datasets for various downstream tasks.Notably, SeisCLIP's performance surpasses that of baseline methods in eventclassification, localization, and focal mechanism analysis tasks, employingdistinct datasets from different regions. In conclusion, SeisCLIP holdssignificant potential as a foundational model in the field of seismology,paving the way for innovative directions in foundation-model-based seismologyresearch.

SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction